DuSK: A dual structure-preserving kernel for supervised tensor learning with applications to neuroimages

Lifang He, Xiangnan Kong, Philip S. Yu, Ann B. Ragin, Zhifeng Hao, Xiaowei Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

27 Scopus citations

Abstract

With advances in data collection technologies, tensor data is assuming increasing prominence in many applications and the problem of supervised tensor learning has emerged as a topic of critical significance in the data mining and machine learning community. Conventional methods for supervised tensor learning mainly focus on learning kernels by flattening the tensor into vectors or matrices, however structural information within the tensors will be lost. In this paper, we introduce a new scheme to design structure-preserving kernels for supervised tensor learning. Specifically, we demonstrate how to leverage the naturally available structure within the tensorial representation to encode prior knowledge in the kernel. We proposed a tensor kernel that can preserve tensor structures based upon dual-tensorial mapping. The dual-tensorial mapping function can map each tensor instance in the input space to another tensor in the feature space while preserving the tensorial structure. Theoretically, our approach is an extension of the conventional kernels in the vector space to tensor space. We applied our novel kernel in conjunction with SVM to real-world tensor classification problems including brain fMRI classification for three different diseases (i.e., Alzheimer's disease, ADHD and brain damage by HIV). Extensive empirical studies demonstrate that our proposed approach can effectively boost tensor classification performances, particularly with small sample sizes. Copyright

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining 2014, SDM 2014
EditorsMohammed J. Zaki, Arindam Banerjee, Srinivasan Parthasarathy, Pang Ning-Tan, Zoran Obradovic, Chandrika Kamath
PublisherSociety for Industrial and Applied Mathematics Publications
Pages127-135
Number of pages9
ISBN (Electronic)9781510811515
DOIs
StatePublished - Jan 1 2014
Event14th SIAM International Conference on Data Mining, SDM 2014 - Philadelphia, United States
Duration: Apr 24 2014Apr 26 2014

Publication series

NameSIAM International Conference on Data Mining 2014, SDM 2014
Volume1

Other

Other14th SIAM International Conference on Data Mining, SDM 2014
CountryUnited States
CityPhiladelphia
Period4/24/144/26/14

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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  • Cite this

    He, L., Kong, X., Yu, P. S., Ragin, A. B., Hao, Z., & Yang, X. (2014). DuSK: A dual structure-preserving kernel for supervised tensor learning with applications to neuroimages. In M. J. Zaki, A. Banerjee, S. Parthasarathy, P. Ning-Tan, Z. Obradovic, & C. Kamath (Eds.), SIAM International Conference on Data Mining 2014, SDM 2014 (pp. 127-135). (SIAM International Conference on Data Mining 2014, SDM 2014; Vol. 1). Society for Industrial and Applied Mathematics Publications. https://doi.org/10.1137/1.9781611973440.15